On cherche à étudier l’effet de trois facteurs sur le transcriptome des racines d’Arabidopsis thaliana et de la micro Tomate.

CO2

Clustering

****************************************
coseq analysis: Poisson approach & none transformation
K = 2 to 12 
Use set.seed() prior to running coseq for reproducible results.
****************************************
Running g = 2 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0"
Running g = 3 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0"
Running g = 4 ...
[1] "Initialization: 1"
[1] "Log-like diff: 9.85664883046411e-11"
Running g = 5 ...
[1] "Initialization: 1"
[1] "Log-like diff: 5.6843418860808e-13"
Running g = 6 ...
[1] "Initialization: 1"
[1] "Log-like diff: 5.98125211581646e-08"
Running g = 7 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0"
Running g = 8 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0"
Running g = 9 ...
[1] "Initialization: 1"
[1] "Log-like diff: 3.95914412365528e-11"
Running g = 10 ...
[1] "Initialization: 1"
[1] "Log-like diff: 1.28659394249553e-09"
Running g = 11 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0"
Running g = 12 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.0035332049280612"
[1] "Log-like diff: 0.000485432284904164"
[1] "Log-like diff: 6.66189752109858e-05"
[1] "Log-like diff: 9.14113201133659e-06"
$ICL


$profiles


$boxplots


$probapost_barplots


*************************************************
Model: Poisson
Transformation: none
*************************************************
Clusters fit: 2,3,4,5,6,7,8,9,10,11,12
Clusters with errors: ---
Selected number of clusters via ICL: 12
ICL of selected model: -740101.6
*************************************************
Number of clusters = 12
ICL = -740101.6
*************************************************
Cluster sizes:
 Cluster 1  Cluster 2  Cluster 3  Cluster 4  Cluster 5  Cluster 6  Cluster 7 
        11         11         20         12         13         18          6 
 Cluster 8  Cluster 9 Cluster 10 Cluster 11 Cluster 12 
        10          9          5         14          2 

Number of observations with MAP > 0.90 (% of total):
131 (100%)

Number of observations with MAP > 0.90 per cluster (% of total per cluster):
 Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7
 11        11        20        12        13        18        6        
 (100%)    (100%)    (100%)    (100%)    (100%)    (100%)    (100%)   
 Cluster 8 Cluster 9 Cluster 10 Cluster 11 Cluster 12
 10        9         5          14         2         
 (100%)    (100%)    (100%)     (100%)     (100%)    

Visualisation en ACP

Class: pca dudi
Call: dudi.pca(df = log(data + 0.1), center = TRUE, scale = TRUE, scannf = FALSE, 
    nf = 4)

Total inertia: 24

Eigenvalues:
    Ax1     Ax2     Ax3     Ax4     Ax5 
17.2837  4.7082  0.6320  0.5028  0.2617 

Projected inertia (%):
    Ax1     Ax2     Ax3     Ax4     Ax5 
 72.015  19.617   2.633   2.095   1.090 

Cumulative projected inertia (%):
    Ax1   Ax1:2   Ax1:3   Ax1:4   Ax1:5 
  72.02   91.63   94.27   96.36   97.45 

(Only 5 dimensions (out of 24) are shown)

NULL

Réseau

# Nitrate

****************************************
coseq analysis: Poisson approach & none transformation
K = 2 to 12 
Use set.seed() prior to running coseq for reproducible results.
****************************************
Running g = 2 ...
[1] "Initialization: 1"
[1] "Log-like diff: 4.59245086403826e-10"
Running g = 3 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.02732707907575"
[1] "Log-like diff: 0.000268834437022747"
[1] "Log-like diff: 3.19062163711692e-06"
Running g = 4 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.00317565071129877"
[1] "Log-like diff: 6.45337854905392e-05"
[1] "Log-like diff: 1.32643040373637e-06"
Running g = 5 ...
[1] "Initialization: 1"
[1] "Log-like diff: 780.379968968336"
[1] "Log-like diff: 543.197241442135"
[1] "Log-like diff: 752.743987958856"
[1] "Log-like diff: 533.723082605684"
[1] "Log-like diff: 94.16844828535"
Running g = 6 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.429716284972326"
[1] "Log-like diff: 0.0110679937471261"
[1] "Log-like diff: 0.000294696082274726"
[1] "Log-like diff: 7.96987497508894e-06"
Running g = 7 ...
[1] "Initialization: 1"
[1] "Log-like diff: 6.9358451781909e-07"
Running g = 8 ...
[1] "Initialization: 1"
[1] "Log-like diff: 1.48705296112439e-05"
[1] "Log-like diff: 4.44065548776962e-06"
Running g = 9 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.187270797612783"
[1] "Log-like diff: 0.0804456928053696"
[1] "Log-like diff: 0.0318910951551601"
[1] "Log-like diff: 0.0125871810649834"
[1] "Log-like diff: 0.00495944033215423"
Running g = 10 ...
[1] "Initialization: 1"
[1] "Log-like diff: 4.49220970679676e-05"
[1] "Log-like diff: 5.0755175600159e-06"
Running g = 11 ...
[1] "Initialization: 1"
[1] "Log-like diff: 2.50112567030669e-05"
[1] "Log-like diff: 1.44340664931519e-06"
Running g = 12 ...
[1] "Initialization: 1"
[1] "Log-like diff: 3.99837858111596e-06"
$ICL


$profiles


$boxplots


$probapost_barplots


*************************************************
Model: Poisson
Transformation: none
*************************************************
Clusters fit: 2,3,4,5,6,7,8,9,10,11,12
Clusters with errors: ---
Selected number of clusters via ICL: 12
ICL of selected model: -3013888
*************************************************
Number of clusters = 12
ICL = -3013888
*************************************************
Cluster sizes:
 Cluster 1  Cluster 2  Cluster 3  Cluster 4  Cluster 5  Cluster 6  Cluster 7 
       109         24        105         49        122          7        121 
 Cluster 8  Cluster 9 Cluster 10 Cluster 11 Cluster 12 
        35         21        158         32         54 

Number of observations with MAP > 0.90 (% of total):
836 (99.88%)

Number of observations with MAP > 0.90 per cluster (% of total per cluster):
 Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7
 109       24        105       49        122       7         121      
 (100%)    (100%)    (100%)    (100%)    (100%)    (100%)    (100%)   
 Cluster 8 Cluster 9 Cluster 10 Cluster 11 Cluster 12
 35        21        157        32         54        
 (100%)    (100%)    (99.37%)   (100%)     (100%)    
Class: pca dudi
Call: dudi.pca(df = log(data + 0.1), center = TRUE, scale = TRUE, scannf = FALSE, 
    nf = 4)

Total inertia: 24

Eigenvalues:
    Ax1     Ax2     Ax3     Ax4     Ax5 
19.1693  3.3531  0.5281  0.3930  0.1205 

Projected inertia (%):
    Ax1     Ax2     Ax3     Ax4     Ax5 
 79.872  13.971   2.200   1.638   0.502 

Cumulative projected inertia (%):
    Ax1   Ax1:2   Ax1:3   Ax1:4   Ax1:5 
  79.87   93.84   96.04   97.68   98.18 

(Only 5 dimensions (out of 24) are shown)

NULL

Iron

****************************************
coseq analysis: Poisson approach & none transformation
K = 2 to 12 
Use set.seed() prior to running coseq for reproducible results.
****************************************
Running g = 2 ...
[1] "Initialization: 1"
[1] "Log-like diff: 1.07117870129514e-10"
Running g = 3 ...
[1] "Initialization: 1"
[1] "Log-like diff: 6.38532487400312e-05"
[1] "Log-like diff: 6.09656778216561e-06"
Running g = 4 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.0111171821348677"
[1] "Log-like diff: 0.00141141140834478"
[1] "Log-like diff: 0.000161399031195941"
[1] "Log-like diff: 2.23980092322051e-05"
[1] "Log-like diff: 2.78977087120325e-06"
Running g = 5 ...
[1] "Initialization: 1"
[1] "Log-like diff: 3567.60751860177"
[1] "Log-like diff: 3726.79928110929"
[1] "Log-like diff: 1847.6149884063"
[1] "Log-like diff: 8431.18437529454"
[1] "Log-like diff: 2533.24931846412"
Running g = 6 ...
[1] "Initialization: 1"
[1] "Log-like diff: 61.6607030025398"
[1] "Log-like diff: 220.279999620299"
[1] "Log-like diff: 343.597475596329"
[1] "Log-like diff: 118.657798253033"
[1] "Log-like diff: 89.3377189676472"
Running g = 7 ...
[1] "Initialization: 1"
[1] "Log-like diff: 294.715502575396"
[1] "Log-like diff: 150.39616865371"
[1] "Log-like diff: 197.001670751701"
[1] "Log-like diff: 20.6833498346011"
[1] "Log-like diff: 6.69058247831302"
Running g = 8 ...
[1] "Initialization: 1"
[1] "Log-like diff: 43.5314394208409"
[1] "Log-like diff: 327.586113284712"
[1] "Log-like diff: 500.912263648639"
[1] "Log-like diff: 156.206329141795"
[1] "Log-like diff: 184.589709254823"
Running g = 9 ...
[1] "Initialization: 1"
[1] "Log-like diff: 481.077552880626"
[1] "Log-like diff: 58.6942035806858"
[1] "Log-like diff: 992.988620924576"
[1] "Log-like diff: 30.1221236683701"
[1] "Log-like diff: 235.436537028793"
Running g = 10 ...
[1] "Initialization: 1"
[1] "Log-like diff: 477.227841689411"
[1] "Log-like diff: 126.914316995362"
[1] "Log-like diff: 516.989617982976"
[1] "Log-like diff: 211.394809733677"
[1] "Log-like diff: 38.5426970431339"
Running g = 11 ...
[1] "Initialization: 1"
[1] "Log-like diff: 1.94951214516996"
[1] "Log-like diff: 185.070654937831"
[1] "Log-like diff: 28.381146970325"
[1] "Log-like diff: 26.0836450267923"
[1] "Log-like diff: 44.8672622161952"
Running g = 12 ...
[1] "Initialization: 1"
[1] "Log-like diff: 145.645179740505"
[1] "Log-like diff: 37.4337944851441"
[1] "Log-like diff: 391.669701764039"
[1] "Log-like diff: 396.307727963539"
[1] "Log-like diff: 300.789765029271"
$ICL


$profiles


$boxplots


$probapost_barplots


*************************************************
Model: Poisson
Transformation: none
*************************************************
Clusters fit: 2,3,4,5,6,7,8,9,10,11,12
Clusters with errors: ---
Selected number of clusters via ICL: 12
ICL of selected model: -3391000
*************************************************
Number of clusters = 12
ICL = -3391000
*************************************************
Cluster sizes:
 Cluster 1  Cluster 2  Cluster 3  Cluster 4  Cluster 5  Cluster 6  Cluster 7 
       688        645        110        220        133         30         73 
 Cluster 8  Cluster 9 Cluster 10 Cluster 11 Cluster 12 
       303        122        110        366         41 

Number of observations with MAP > 0.90 (% of total):
2773 (97.61%)

Number of observations with MAP > 0.90 per cluster (% of total per cluster):
 Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7
 682       638       105       215       122       28        73       
 (99.13%)  (98.91%)  (95.45%)  (97.73%)  (91.73%)  (93.33%)  (100%)   
 Cluster 8 Cluster 9 Cluster 10 Cluster 11 Cluster 12
 291       118       107        354        40        
 (96.04%)  (96.72%)  (97.27%)   (96.72%)   (97.56%)  
Class: pca dudi
Call: dudi.pca(df = log(data + 0.1), center = TRUE, scale = TRUE, scannf = FALSE, 
    nf = 4)

Total inertia: 24

Eigenvalues:
     Ax1      Ax2      Ax3      Ax4      Ax5 
22.03089  1.11520  0.27107  0.11321  0.06945 

Projected inertia (%):
    Ax1     Ax2     Ax3     Ax4     Ax5 
91.7954  4.6467  1.1295  0.4717  0.2894 

Cumulative projected inertia (%):
    Ax1   Ax1:2   Ax1:3   Ax1:4   Ax1:5 
  91.80   96.44   97.57   98.04   98.33 

(Only 5 dimensions (out of 24) are shown)

NULL

 

A work by Océane Cassan

oceane.cassan@supagro.fr